Hi there !
I'm Ratna Sari Pratiwi

Data Analyst

I am a passionate and dedicated Data Analyst with over two years of experience in operations management and customer service, eager to leverage my strong problem-solving mindset and data-driven approach to deliver high-quality results. I have enhanced my technical expertise by completing the RevoU Full-Stack Data Analytics program, where I gained hands-on experience with Python, SQL, Excel, and Tableau to analyze and visualize data to drive business strategies.

Education

Sekolah Tinggi Pariwisata NHI Bandung — Bachelor of Tourism (2016 - 2020)
GPA: 3.29**
- Gained valuable insights into customer behavior, service excellence, and business operations.RevoU Full-Stack Data Analytics Program (May 2024 - September 2024)
- Developed practical skills in Python, SQL, Tableau, and Excel.
- Completed a customer booking analysis project for Kiwi.com, where I analyzed 50,000 bookings, identified trends, and recommended dynamic pricing and personalized marketing strategies.

Work Experience

PT Angkut Teknologi Indonesia (Deliveree Indonesia) — Booking Operation Center Specialist
(Sep 2022 - Present)
- Focused on optimizing booking processes and analyzing performance metrics.
- Applied a tracking system that improved on-time deliveries by 20%, enhancing operational efficiency.
- Reduced response times by 30% through better communication channels, boosting customer satisfaction.Kebab Turki Baba Rafi, Transmart Buah Batu — Store Supervisor (Feb 2022 - Aug 2022)
- Ensured 80% product availability and reduced stock loss by 10% through accurate inventory management.
- Created monthly financial reports with 100% accuracy using Excel, helping ensure budget adherence.
- Increased staff productivity by 10% through training programs and schedule optimization.Anzu Gaming Lab — Store Supervisor (Mar 2020 - Dec 2021)
- Achieved 95% inventory accuracy, ensuring product availability.
- Enhanced customer satisfaction by 100% through service process improvements and product recommendations.

Skills

- Python, SQL, Tableau, Excel
- Freshchat, xCally, Ragic
- Strong analytical mindset, problem-solving, communication, and teamwork skills

Projects

Analysis of Customer Booking

Study Case of Kiwi.com

This project analyzes Kiwi.com’s booking data to identify trends and strategies for doubling bookings from 50,000 in 2022 to 100,000 by 2023. Key recommendations include dynamic pricing, targeted promotions for high-demand routes, and incentives for mobile bookings to drive customer engagement.

Check here

About this project

This project analyze customer booking patterns on Kiwi.com to develop data-driven strategies for increasing bookings from 50,000 in 2022 to 100,000 by the end of 2023. By examining key factors such as popular routes, booking trends by day, and customer preferences, the study identifies opportunities to optimize pricing, enhance promotions, and improve user engagement. The insights gathered will help Kiwi.com implement targeted strategies, including dynamic pricing, tailored promotions for high-demand routes, and incentives for mobile bookings, ultimately driving growth in customer conversions.

Kiwi.com is a leading travel technology company offering an online flight booking platform. In 2022, it processed 50,000 bookings and set a goal to double this to 100,000 by 2023. Achieving this requires a data-driven strategy focused on analyzing booking patterns, understanding customer behavior, and optimizing booking channels.

To help Kiwi.com double its bookings from 50,000 to 100,000, I employed a structured, data-driven approach utilizing Microsoft Excel, Python, and Tableau. I began by collecting booking data from 2022, followed by a thorough cleaning process in Microsoft Excel to ensure data integrity. After preparing the dataset, I conducted Exploratory Data Analysis (EDA) using Python, which allowed me to uncover key trends in popular routes, peak booking days, and customer preferences. Additionally, I performed correlation analysis to assess relationships between trip length and add-on purchases. This analysis provided predictive insights that guided the development of dynamic pricing strategies, targeted marketing campaigns, and personalized add-on bundles. Finally, I visualized the findings through interactive dashboards created in Tableau, delivering clear and actionable insights to optimize customer engagement and drive revenue growth.

Results & Findings
The analysis revealed that the Asia-Oceania route had the highest booking volume, with the Auckland to Kuala Lumpur (AKL-KUL) route being the most popular, recording 2,620 bookings. It was observed that peak booking days occurred from Monday to Wednesday, while activity on weekends, particularly Saturdays, was significantly lower. Furthermore, internet bookings vastly outperformed mobile bookings, indicating a noticeable gap in mobile engagement. Lastly, the analysis found a weak correlation between trip length and add-on purchases, suggesting that other factors may have a more substantial influence on customer preferences.

Strategic Impact
Based on these insights, dynamic pricing was recommended to adjust fares based on demand fluctuations, with higher pricing on peak booking days and discounts on low-demand weekends. Additionally, targeted marketing campaigns were proposed to boost mobile engagement, including exclusive mobile discounts, rewards points, and bundled add-on offers. To further strengthen the already popular Asia-Oceania routes, tailored travel offers aimed at both business and leisure travelers were suggested. These findings provide a comprehensive, data-driven strategy to enhance customer engagement, optimize pricing, and drive booking growth.